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GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations

  • Neurogenetics and Psychiatric Genetics (C Cruchaga and C Karch, Section Editors)
  • Published:
Current Genetic Medicine Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Neurodegenerative diseases, neuropsychiatric disorders, and related traits have highly complex etiologies but are also highly heritable; identifying the causal genes and biological pathways underlying these traits may advance the development of treatments and preventive strategies. While many genome-wide association studies (GWAS) have successfully identified variants contributing to polygenic neurodegenerative and neuropsychiatric phenotypes including Alzheimer’s disease (AD), schizophrenia (SCZ), and bipolar disorder (BPD) among others, interpreting the biological roles of significantly associated variants in the genetic architecture of these traits remains a significant challenge. Here, we review several ‘omics’ approaches which attempt to bridge the gap from associated genetic variants to phenotype by helping define the functional roles of GWAS loci in the development of neuropsychiatric disorders and traits.

Recent Findings

Several common ‘omics’ approaches have been applied to examine neuropsychiatric traits, such as nearest-gene mapping, trans-ethnic fine mapping, annotation enrichment analysis, transcriptomic analysis, and pathway analysis, and each of these approaches has strengths and limitations in providing insight into biological mechanisms. One popular emerging method is the examination of tissue-specific genetically regulated gene expression (GReX), which aggregates the genetic variants’ effects at the gene level. Furthermore, proteomic, metabolomic, and microbiomic studies and phenome-wide association studies will further enhance our understanding of neuropsychiatric traits.

Summary

GWAS has been applied to neuropsychiatric traits for a decade, but our understanding about the biological function of identified variants remains limited. Today, technological advancements have created analytical approaches for integrating transcriptomics, metabolomics, proteomics, pharmacology, and toxicology as tools for understanding the functional roles of genetic variants. These data, as well as the broader clinical information provided by electronic health records, can provide additional insight and complement genomic analyses.

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Funding

W. Bush reports grants from NIH/HIA U54 AG052427, A.C. Naj reports grants R01 AG054060 and U01 AG032984, and H-H. Chen, L.E. Petty, W, Bush, A.C. Naj, and J.E. Below are supported by R01AG061351.

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    1. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA

      Hung-Hsin Chen, Lauren E. Petty & Jennifer E. Below

    2. Department of Population and Quantitative Health Sciences, School of Medicine, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA

      William Bush

    3. Department of Biostatistics, Epidemiology, and Informatics; Department of Pathology and Laboratory Medicine; Center for Clinical Epidemiology and Biostatistics; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

      Adam C. Naj

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    Jennifer E. Below, Will Bush and Adam Naj contributed equally to this work.

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    Chen, HH., Petty, L.E., Bush, W. et al. GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations. Curr Genet Med Rep 7, 30–40 (2019). https://doi.org/10.1007/s40142-019-0159-z

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